Prenatal Substance Use and Perceptions of Parent and Partner Use Using the 4P’s Plus Screener

Maternal and Child Health Journal - Background Prenatal substance use screening is recommended. The 4 P’s Plus screener includes questions on perceived problematic substance use in parents...     

Motion‐compensated b‐tensor encoding for in vivo cardiac diffusion‐weighted imaging

Motion is a major confound in diffusion‐weighted imaging (DWI) in the body, and it is a common cause of image artefacts. The effects are particularly severe in cardiac applications, due to the nonrigid cyclical deformation of the myocardium. Spin echo‐based DWI commonly employs gradient moment‐nulling techniques to desensitise the acquisition to velocity and acceleration, ie, nulling gradient moments up to the 2nd order (M2‐nulled). However, current M2‐nulled DWI scans are limited to encode diffusion along a single direction at a time. We propose a method for designing b‐tensors of arbitrary shapes, including planar, spherical, prolate and oblate tensors, while nulling gradient moments up to the 2nd order and beyond. The design strategy comprises initialising the diffusion encoding gradients in two encoding blocks about the refocusing pulse, followed by appropriate scaling and rotation, which further enables nulling undesired effects of concomitant gradients. Proof‐of‐concept assessment of in vivo mean diffusivity (MD) was performed using linear and spherical tensor encoding (LTE and STE, respectively) in the hearts of five healthy volunteers. The results of the M2‐nulled STE showed that (a) the sequence was robust to cardiac motion, and (b) MD was higher than that acquired using standard M2‐nulled LTE, where diffusion‐weighting was applied in three orthogonal directions, which may be attributed to the presence of restricted diffusion and microscopic diffusion anisotropy. Provided adequate signal‐to‐noise ratio, STE could significantly shorten estimation of MD compared with the conventional LTE approach. Importantly, our theoretical analysis and the proposed gradient waveform design may be useful in microstructure imaging beyond diffusion tensor imaging where the effects of motion must be suppressed.     

The Relationship between Self-Efficacy,Functional Exercise Capacity and Physical Activity in People with COPD: A Systematic Review and Meta-Analyses

Abstract

The purpose of this study was to investigate the strength of the relationships between self-efficacy and (i) functional exercise capacity and (ii) physical activity in chronic obstructive pulmonary disease (COPD), and whether self-efficacy assessment type (i.e., COPD symptoms, exercise-task, exercise-barrier, general, falls) and physical activity assessment type (i.e., self-report vs. objective) are moderators. A systematic search of COPD and self-efficacy concepts was conducted using eight databases from inception to 23 January 2019. Studies were included if they provided correlation coefficients of the relationship between self-efficacy and functional exercise capacity or physical activity, were conducted in adults diagnosed with COPD, and were published in English-language journals. A total of 14 correlation coefficients were included in the self-efficacy and functional exercise capacity meta-analysis, and 16 in the self-efficacy and physical activity meta-analysis. Data were screened, reviewed, and extracted independently by two reviewers, with discrepancies resolved by a third reviewer. Stronger self-efficacy was associated with better functional exercise capacity (weighted r?=?0.38, 95%CI [0.25, 0.50]), and greater physical activity (weighted r?=?0.25, 95%CI [0.17, 0.34]). Exercise-task self-efficacy had the strongest relationship to functional exercise capacity (weighted r?=?0.64, 95% CI [0.51, 0.73]). For physical activity, the type of self-efficacy most strongly related was inconclusive. In COPD, self-efficacy has a relationship to functional exercise capacity and physical activity, the strength of which is influenced by the choice of self-efficacy measure. An understanding of these relationships will assist clinicians in selecting the self-efficacy measure most closely related to the outcome of interest.